Description
Mastercard's Business & Market Insights (B&MI) team creates products that help customers understand their customers and the market better.
The Senior Data Scientist (AI, ML, Pyspark, SQL) will work across Product, Client Services & Delivery, Data & Engineering and the broader teams to deliver truly differentiated products driven by analytics & AI.
Responsibilities:
- Design, develop, and productionize machine learning and data science solutions that solve complex business problems within BMI.
- Develop traditional ML solutions covering feature engineering, model training, validation, evaluation, and deployment.
- Perform exploratory data analysis and translate complex datasets into actionable insights.
- Optimize data pipelines and ML workflows for performance, scalability, maintainability, and reliability.
- Develop robust and reusable Python components following sound software-engineering practices.
- Use PySpark to process, transform, and analyze large datasets efficiently in distributed environments.
- Write advanced, optimized SQL for data extraction, transformation, feature development, and analytical workloads.
- Apply appropriate controls for data privacy, security, responsible AI, monitoring, and explainability.
Good to have:
- Understanding of building Generative AI applications using Python and modern GenAI frameworks.
The successful candidate's primary responsibilities will be to:
- Drive the evolution of Business & Market Insights products by applying Data Science and Artificial Intelligence to create models, tools and capabilities.
- Participate in the exploration, application and development of data science for product development.
- Participate in identification and evaluation of AI/ML techniques and tools to deliver models and analytics for product development.
- Continuously innovate and determine new data science approaches & technologies to solve business problems and generate business insights & recommendations.
- Partner with roles across the organization including product, engineering, and sales to determine the highest priority data science problems to solve in a customer-centric framework.
- Evaluate trade-offs between many possible analytics solutions to a problem, taking into account performance, usability, technical feasibility, timelines, and differing stakeholder opinions to make a decision.
- Break large solutions into smaller, releasable milestones to collect data and feedback from product managers, clients and other stakeholders.
- Evangelize releases to users, incorporating feedback, and tracking usage to inform future development.
- Work with small, cross-functional teams to define vision, establish team culture and processes.
- Consistently focus on key drivers of organization value and prioritize operational activities accordingly.
- Identify and act upon product improvement opportunities.
- Provide technical leadership of junior colleagues as a senior member of the data science team, through technical and non-technical mentoring.